成果归属作者：

管建和

成果归属机构：

信息工程学院

作者：

Cheng, Shiyu ; Shang, Pan ; Zhang, Yingwei ; Guan, Jianhe ; Chen, Yiqiang ; Lv, Zeping ; Huang, Shuyun ; Liu, Yajing ; Xie, Haiqun

单位：

Chinese Acad Sci, Beijing Key Lab Mobile Comp & Pervas Device, Inst Comp Technol, Beijing 100190, Peoples R China;Univ Chinese Acad Sci, Beijing 101408, Peoples R China;China Univ Geosci, Beijing 100083, Peoples R China;First Peoples Hosp Foshan, Dept Neurol, Foshan 528010, Guangdong, Peoples R China;Natl Res Ctr Rehabil Tech Aids, Beijing Key Lab Rehabil Tech Aids Old Age Disabil, Beijing 100176, Peoples R China

关键词：

STATE; MCI; CLASSIFICATION; CONVERSION; DEMENTIA

摘要：

Amnestic mild cognitive impairment (aMCI) is the prodromal period of more serious neurodegenerative diseases (e.g., Alzheimer's disease), characterized by declines in memory and thinking abilities. Auxiliary assessment and early diagnosis of aMCI are crucial in preventing the continued deterioration of cognitive abilities; nevertheless, this task poses a formidable challenge due to the inconspicuous nature of early symptoms. Functional near-infrared spectroscopy (fNIRS) is a non-invasive, low-cost, and user-friendly neuroimaging technique, which is capable of detecting subtle changes in brain activity among different subjects. Moreover, multimodal fusion can assess cognition status from different perspectives and enhance auxiliary diagnosis accuracy significantly. This paper proposes an fNIRS representation and fNIRS-scales multimodal fusion method for auxiliary diagnosis of aMCI. Specifically, we convert one-dimensional time-series fNIRS signals into two-dimensional images with Gramian Angular Field and achieve end-to-end fNIRS representation with convolutional neural network. Then, we integrate the extracted features with cognitive scales at the decision-making level to improve the diagnosis accuracy of aMCI, employing the data balance strategy to prevent biased prediction. What is more, based on the fNIRS features, we also propose a data-driven cognitive scales-screening method to help the physician to assess aMCI with higher efficiency. We conducted experiments on 86 subjects (including 53 aMCI patients and 33 normal controls) recruited from Foshan First People's Hospital. The diagnosis accuracy reaches 88.02% and 93.90% with fNIRS representation and further fNIRSscales fusion, respectively. With the cognitive scales-screening, we delete 50% scales, reducing test time but only losing 2.54% accuracy.

基金：

Natural Science Foundation of China [62302487]; Beijing Municipal Science & Technology Commission [Z221100002722009]; Science and Technology Innovation Program of Hunan Province [2024JJ9031, 2022RC4006]

语种：

英文

DOI：

10.1016/j.bspc.2024.106646

来源：

BIOMEDICAL SIGNAL PROCESSING AND CONTROL,2024():.

出版日期：

2024-10

提交日期：

2024-08-10

引用参考：

Cheng, Shiyu; Shang, Pan; Zhang, Yingwei; Guan, Jianhe; Chen, Yiqiang; Lv, Zeping; Huang, Shuyun; Liu, Yajing; Xie, Haiqun. An fNIRS representation and fNIRS-scales multimodal fusion method for auxiliary diagnosis of amnestic mild cognitive impairment[J]. BIOMEDICAL SIGNAL PROCESSING AND CONTROL,2024():.